Impact of Speckle Filtering on the Decomposition and Classification of Fully Polarimetric RADARSAT-2 Data

  • Sivasubramanyam MedasaniEmail author
  • G. Umamaheswara Reddy
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Decomposition and classification are vital processing stages in polarimetric synthetic aperture radar (PolSAR) information processing. Speckle noise affects SAR data since backscattered signals from various targets are coherently integrated. Current study investigated the impact of speckle suppression on the target decomposition and classification of RADARSAT-2 fully polarimetric data. Speckle filters should suppress the speckle noise along with the retention of spatial and polarimetric information. The performance of improved Lee–Sigma, intensity-driven adaptive neighborhood (IDAN), refined Lee, and boxcar filters were assessed utilizing the spaceborne dataset, that is, fully polarimetric RADARSAT-2 C-band SAR data for the Mumbai region, India. The effect of speckle suppression on target decomposition was analyzed in this study. Different speckle noise suppression techniques were applied to RADARSAT-2 dataset, followed by Yamaguchi three-component and VanZyl decompositions. The obtained findings revealed that the improved Lee–Sigma filter demonstrated better volume scatterings in forest areas and double bounce in urban areas than the other techniques considered in the analysis. Additionally, the efficacy of the different speckle suppression techniques listed above was assessed. The effectiveness of the speckle filtering algorithm was evaluated by applying the Wishart supervised classification to the filtered and unfiltered data. IDAN, boxcar, refined Lee, and improved Lee–Sigma filters were assessed to find the classification accuracy improvement. A considerable amount of improvement was observed in the classification accuracy for mangrove and forest classes. Minimal enhancement was detected for settlement, bare soil, and water classes.


Polarimetry Synthetic aperture radar Polarimetric synthetic aperture radar Speckle noise Decomposition and classification 



The authors are grateful to Space Application Centre, ISRO, India for giving the opportunity to carry out research work and providing the data under TREES. The authors are thankful to Dr. Anup Kumar Das, SAC, ISRO for providing the guidance to conduct the research. The authors are thankful to Dr. C. V. Rao, NRSC, ISRO for his constant support and encouragement. The authors are grateful to the Centre of Excellence and Department of Electronics and Communication Engineering at Sri Venkateswara University College of Engineering for providing the resources. Furthermore, the authors are thankful to Mr. P. Anil Kumar, Mr. C. Raju, Mr. N. Chintaiah, and research scholars for the valued discussions and encouragement. The authors would like to thank the European Space Agency for providing the open-source software and the experimental data of the PolSARpro project.


  1. 1.
    Lee JS, Pottier E (2009) Polarimetric radar imaging: from basics to applications. CRC Press, ClevelandCrossRefGoogle Scholar
  2. 2.
    Cloude SR (2009) Polarisation applications in remote sensing. Oxford University Press, OxfordCrossRefGoogle Scholar
  3. 3.
    Foucher S, López-Martínez C (2014) Analysis, evaluation, and comparison of polarimetric SAR speckle filtering techniques. IEEE Trans Image Process 23(4):1751–1764MathSciNetCrossRefGoogle Scholar
  4. 4.
    Argenti F, Lapini A, Alparone L, Bianchi T (2013) A tutorial on speckle reduction in synthetic aperture radar images. IEEE Geosci Remote Sens Mag 1:6–35CrossRefGoogle Scholar
  5. 5.
    Di Martino G, Poderico M, Poggi G, Riccio D, Verdoliva L (2014) Benchmarking framework for SAR despeckling. IEEE Trans Geosci Remote Sens 52(3):1596CrossRefGoogle Scholar
  6. 6.
    Cloude SR, Pottier E (1996) A review of target decomposition theorems in radar polarimetry. IEEE Trans Geosci Remote Sens 34(2):498–518CrossRefGoogle Scholar
  7. 7.
    Freeman A, Durden S (1998) A three-component scattering model for polarimetric SAR data. IEEE Trans Geosci Remote Sens 36(3):963–973CrossRefGoogle Scholar
  8. 8.
    Yamaguchi Y, Moriyama T, Ishido M, Yamada H (2005) Fourcomponent scattering model for polarimetric SAR image decomposition. IEEE Trans Geosci Remote Sens 43(8):1699–1706CrossRefGoogle Scholar
  9. 9.
    Van Zyl JJ (1992) Application of Cloude’s target decomposition theorem to polarimetric imaging radar data. In: Proceedings SPIE conference on radar polarimetry, San Diego, CA, vol 1748, pp 184–212Google Scholar
  10. 10.
    Medasani S, Umamaheswara Reddy G (2018) Speckle filtering and its influence on the decomposition and classification of hybrid polarimetric data of RISAT-1. Remote Sens Appl: Environ Soc 10:1–6Google Scholar
  11. 11.
    Lee JS, Grunes MR, Kwok R (1994) Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution. Int J Remote Sens 15(11):2299–2311CrossRefGoogle Scholar
  12. 12.
    Lee JS, Grunes MR, Ainsworth TL, Li-Jen D, Schuler DL, Cloude SR (1999) Unsupervised classification using polarimetric decomposition and the complex Wishart classifier. IEEE Trans Geosci Remote Sens 37(5):2249–2258CrossRefGoogle Scholar
  13. 13.
    Ferro-Famil L, Pottier E, Lee JS (2001) Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H/A/Alpha-Wishart classifier. IEEE Trans Geosci Remote Sens 39(11):2332–2342CrossRefGoogle Scholar
  14. 14.
    Shitole S, De S, Rao YS, Mohan BK, Das A (2015) Selection of suitable window size for speckle reduction and deblurring using SOFM in polarimetric SAR images. J Indian Soc Remote Sens 43(4):739–750CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sivasubramanyam Medasani
    • 1
    Email author
  • G. Umamaheswara Reddy
    • 1
  1. 1.Department of Electronics and Communication EngineeringSri Venkateswara University College of Engineering, Sri Venkateswara UniversityTirupatiIndia

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